190 results about "Function computation" patented technology

The invention belongs to the field of machine learning, and discloses an FPGA (Field Programmable Gate Array) superposition processor acceleration system and method based on state space duality, which comprises a sparse predefined data acquirer, a reconfigurable systolic array, a partial sum cache, an element-by-element operation cache, a function calculation module, an on-chip memory management module and the like. Zero elements are eliminated through the sparse predefined data acquirer, redundant data are reduced, and redundant calculation is remarkably reduced; the reconfigurable systolic array flexibly supports multiple calculation modes, and the utilization rate of hardware resources is increased; the part and the cache realize cross-cycle accumulation and element-by-element operation cache integration results, the function calculation module completes nonlinear operation, and the on-chip memory management module optimizes result cache, so that on-chip operation of SSD calculation is ensured, off-chip memory access is remarkably reduced, and reasoning efficiency and energy efficiency are improved; by adopting the system, the memory occupation is effectively reduced, the element-by-element calculation efficiency is improved, and the sparse calculation redundancy is reduced, so that the reasoning process of the Mamba2 model is remarkably accelerated.

The invention relates to the technical field of numerical control programming, in particular to a five-axis machining path planning method and system based on data driving, and the method comprises the following steps: obtaining real-time coordinates of each axis of a machine tool, calculating linear velocity and angular velocity components to construct a Jacobian matrix, executing singular value decomposition, and calculating a conditional number ratio by using maximum and minimum singular values; and inputting a nonlinear mapping function to calculate a dynamic penalty factor, generating a rotating shaft weighted item in combination with a rotating shaft identifier, constructing a weighted damping least square objective function, calculating a five-axis motion increment, and accumulating the five-axis motion increment with a real-time coordinate to generate a target absolute position coordinate. According to the method, the pose singularity degree is quantified by monitoring the machine tool pose condition number ratio and converted into the dynamic penalty factor to apply the self-adaptive constraint to the rotating shaft, the severe sudden change of the rotating shaft in the singularity area is inhibited, the tool nose track following error is minimized, and meanwhile smooth distribution of the motion increment is achieved; and the dynamic stability and the surface quality of five-axis linkage machining are improved.

The invention belongs to the technical field of data visualization, and particularly relates to a dynamic visualization generation method, system and device based on user behaviors and context awareness and a medium, and the method comprises the steps: generating a structured operation sequence after standardizing original input data; performing time sequence modeling on the operation sequence to generate a user behavior semantic vector, and generating a joint feature vector by adopting an attention mechanism; pre-loading an input data set, and generating a candidate view set in combination with the user intention preference in the joint feature vector; calculating view scores through a layout optimization function, and screening an optimal view scheme with the highest score; dynamically simplifying or enhancing the chart component according to the equipment performance and context parameters in the joint feature vector; calling a rendering engine to output a view, and monitoring a user interaction event; and updating the strategy network through a PPO algorithm, and optimizing a subsequent view generation path. The problem of unsmooth interaction caused by a static template is avoided, and the user experience is remarkably improved.

The invention discloses a program fault processing method, device and equipment of a software system and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps that a fault knowledge graph of the software system is acquired, the fault knowledge graph is dynamically constructed based on multi-source heterogeneous data, and the fault knowledge graph is stored in a database; wherein a diversified atlas relationship is stored; performing graph neural network driven association analysis based on the system component dependency relationship and the first association relationship, mining a hidden fault propagation path on a component dependency link, and determining a fault association component and a fault root cause corresponding to the program fault data based on the hidden fault propagation path; based on the fault association component, the fault root cause and the second association relationship, determining a plurality of candidate repair schemes corresponding to the program fault data; and calculating evaluation scores of the plurality of candidate repair schemes through the utility function, sorting according to the evaluation scores, and determining an optimal repair scheme of the software system about the program fault in the plurality of candidate repair schemes.

The invention provides a multi-task model fusion method and device, a medium and a program product. The method comprises the following steps: acquiring a plurality of expert models of different task scenes; taking the parameter of each expert model and the sparse version corresponding to each expert model as an initial particle swarm; calculating a performance score of the fusion model for each task according to a corresponding preset evaluation index, and taking a mean value of the performance scores of all the tasks as an objective function of particle swarm optimization; iteratively updating the initial particle swarm by adopting a particle swarm optimization algorithm, calculating a historical optimal solution and a global optimal solution of each particle based on a target function, and dynamically adjusting the particle speed and parameters; and taking the global optimal particle parameters after iteration as fused model parameters, generating a fusion model, and deploying the fusion model at a target terminal. According to the method, the model fusion efficiency and the multi-task processing capability are improved, and the occupation of the computer storage space is reduced.

The invention discloses an asynchronous federal learning method and system based on dynamic contribution perception and old adaptation, and the method comprises the steps: receiving a corresponding contribution degree after a server broadcasts a latest global model and a contribution degree vector; judging whether the received contribution degree meets a training condition, if so, downloading a latest global model for training, and if not, continuing to use an old version global model for training; fusing the loss function, the model deviation penalty and the old suppression item, and constructing a local optimization objective function; calculating a dynamic learning rate; and executing gradient descent to update the local model, and sending the local model to the server, so that the server performs weighted aggregation on the local model by using the dynamic aggregation weight after receiving the client local model greater than or equal to a preset number, updates the client contribution degree, and performs normalization to generate a contribution degree vector. The method gives consideration to the communication efficiency and the model precision, is suitable for a large-scale distributed machine learning scene, and has the advantages of high robustness and good adaptability.

The invention discloses an opportunity fair federal learning excitation method and system based on the game theory, and the method comprises the steps: a model owner determines a reward value of a current round, determines the contribution rate of participating in the training of the current round according to each data node of the reward value of the current round, and carries out the local model training corresponding to each data node; the model owner aggregates the local models of the data nodes according to the reputation values of the data nodes to obtain a global model; constructing a first utility function according to the overhead of each data point in the current round, and constructing a second utility function according to the earnings of the model owner; and calculating the utility of a model owner and each data node according to the first utility function and the second utility function, and adjusting the contribution rate of the next round of training and the reward value of the next round according to the utility result. The optimal balance between the power data node retraining contribution rate and the training cost is realized, and the training effect and the duration of federal learning are ensured.

The invention relates to the technical field of graph neural networks, in particular to a graph neural network architecture optimization method based on grammar genetic programming, and the method comprises the steps: S1, constructing a grammar rule library of a graph neural network architecture GNN; s2, based on the grammar rule base, generating an initial GNN architecture population through genetic programming; s3, predicting the performance of the individual GNN architecture in the population by using a Gaussian process proxy model; s4, searching an optimal GNN architecture in the syntax tree space through a genetic algorithm; s5, for the individual GNN architecture in the population, calculating a training probability based on an expected improvement function of the individual GNN architecture, if the training probability is higher than a preset evaluation threshold, selecting the individual GNN architecture to carry out actual training evaluation, obtaining the authenticity performance of the individual GNN architecture, and updating the Gaussian process agent model by using the authenticity performance; and S6, judging whether a preset optimization termination condition is met or not, if so, outputting the optimized GNN architecture, and otherwise, returning to the step S3. According to the method, the automation degree, efficiency and model performance of GNN model architecture search can be improved.

The present invention relates to a VDF (initial state, t), comprising: a one-way invertible function S component directly or indirectly constructing the VDF, wherein the S component is defined as an invertible function where a computation delay of forward logic is significantly greater than that of reverse logic; a computation phase: a intermediate states, and / or F(A0, Ab)=S(A0⊕Ab, △) indirectly constructing the VDF; and a verification phase: receiving the initial state and a+1 states, preferably completing a+1 VDF-1 verification tasks by means of parallel (a+1)*(b?b:1) S-1 logic verifications. A VDF construction with salt is preferred; the S component preferably has a structure of F(A, B)=g(A⊕B), wherein g-1 uses sparse tap design. Disclosed are proposed industrial standards VDF32 and VDF8192. The present invention achieves the effects of no initialization parameter phase (Setup); quantum-resistant design; and entropy preservation. Disclosed related uses comprise a randomness beacon, a portal website, a proof-of-replication, a storage service terminal, a cloud computing terminal, two types of blockchain systems, Token encoding and verification, and automatic commitment opening. To support industrial implementation, further disclosed are a VDF computing apparatus, a VDF verification apparatus, a medium, and a chip.

The embodiment of the invention provides a nonlinear function calculation method and a special function calculation unit (SFU). The method comprises the following steps: determining a to-be-fitted function and a fitting interval corresponding to the to-be-fitted function; based on the to-be-fitted function and the fitting interval, configuring segment parameters and linear parameters corresponding to the to-be-fitted function; and performing fitting calculation based on the piecewise parameters and the linear parameters to obtain a first target function corresponding to the to-be-fitted function, and completing nonlinear function calculation based on the first target function. According to the invention, the problems of low precision, poor performance and low efficiency of SFU processing in the prior art are solved, and the effect of improving the SFU processing precision, efficiency and performance is achieved.

The invention discloses a cloud edge-end function calculation unloading method capable of cooperatively transmitting data. According to the method, a function calculation unloading process is divided into a data transmission process and a function calculation process. The function calculation process depends on data transmission among the nodes, so that a collaborative data collaboration mechanism can be introduced in the data transmission process, and the task calculation time delay is further reduced. Compared with the prior art, the method has the advantages that a data unloading mechanism is introduced in the cloud side-end collaborative computing unloading process under the complex function dependency relationship; task calculation time delay is reduced, and calculation efficiency is improved.

The invention discloses an intelligent stereoscopic warehouse inventory data synchronization method and device and a computer program product, and the method comprises the steps: collecting multi-source data of an intelligent stereoscopic warehouse, and constructing a multivariate data set; embedding the multivariate data set into a nonlinear mapping space, and constructing an embedded state vector for expressing inventory behaviors; a scheduling weight evaluation function is constructed based on the embedded state vector and historical error feedback, the scheduling weight of the inventory operation is calculated through the scheduling weight evaluation function, a decision is made on the synchronization mode of the inventory operation according to the scheduling weight, and the synchronization mode comprises instant synchronization, delay synchronization or merging synchronization; and based on the scheduling weight and historical error feedback, dynamically correcting the response tension coefficient of each dimension in the embedded state vector, and applying the corrected response tension coefficient to subsequent embedded state vector construction to form a feedback closed loop. According to the invention, a powerful guarantee is provided for efficient, accurate and stable operation of the intelligent warehousing system.

The embodiment of the invention provides a data processing method, which comprises the following steps of: converting an e index calculation task into an index calculation task aiming at a target fixed point number, and splitting the index calculation task aiming at the target fixed point number into a shift task and index calculation aiming at a target decimal part, an index calculation task aiming at the target decimal part is approximate to linear function calculation (namely addition and multiplication based on calculation parameters), so that the calculation process can be greatly simplified, and simple multiplication and addition are executed by utilizing a digital signal processor based on the calculation parameters corresponding to the target decimal part; compared with the prior art, the method has the advantages that operation steps and required computing resources are reduced, all computing resources of one digital signal processor are fully utilized in the process, the whole task process can be performed in series, judgment branches and data rereading do not exist, the overall computing efficiency is improved, and hardware resource consumption is reduced.

The invention discloses a quantum bit mapping method, device and equipment, a medium and a computer program product, and the method comprises the steps: calculating initial quantum bit mapping according to the mutual relation between quantum bits on which each logic gate in a quantum circuit depends; the initial quantum bit mapping is initial mapping from logic quantum bits to physical quantum bits; generating a to-be-executed gate circuit set according to the execution dependency relationship among the logic gates in the quantum circuit; based on the initial quantum bit mapping, the execution cost of each to-be-executed gate circuit in the to-be-executed gate circuit set is calculated, and the to-be-executed gate circuit with the minimum execution cost is selected as a target execution gate circuit; and based on a preset heuristic cost function, calculating costs of different SWAP strategies of the target execution gate circuit, and determining optimal quantum bit mapping according to the SWAP strategy with the minimum cost. According to the invention, the insertion of the SWAP gate in the quantum circuit is significantly reduced, so that the efficiency and precision of quantum bit mapping are improved.

The invention discloses a robot generative adversarial self-imitation learning method based on large language model feedback, and belongs to the technical field of computer systems based on specific calculation models, and the method comprises the steps: (1) initializing a strategy network, a discriminator network and a demonstration pool; (2) constructing and training a large language model feedback network: obtaining a training sample of the large language model feedback network, and training the large language model feedback network according to the training sample; and (3) an iterative learning step (33): updating the strategy network according to the reward calculated by the new cost function. According to the robot generative adversarial self-imitation learning method based on large language model feedback, the advantages of the large language model in the aspects of semantic comprehension, reasoning and knowledge organization are used as a potential award priori source, and a stable award signal can be provided when the demonstration data quality is poor, so that a strategy exceeding original demonstration is learned.

The invention discloses a hardware-based SOFTMAX function calculation method and device. The hardware-based SOFTMAX function calculation method comprises the following steps: receiving a multi-dimensional input vector corresponding to an SOFTMAX function through an input port; uniformly dividing the multi-dimensional input vector into a plurality of data segments according to an element sequence through a channel distributor, and distributing the data segments to corresponding operation channels; performing an exponential operation on the data segment using the operation channel to generate a local exponential vector; aggregating the local exponent vectors with an accumulator to generate a global exponent sum; distributing the global index sum to each operation channel, so that the operation channels generate output sub-vectors according to the local index vectors and the global index sum; and splicing the output sub-vectors according to the element sequence through a recombination unit to generate an SOFTMAX output vector. The efficient SOFTMAX function hardware acceleration calculation under the multi-channel parallel pipeline processing architecture is realized, and the calculation performance and throughput are improved by several times compared with the traditional serial software implementation.

The invention discloses a background vehicle anthropomorphic trajectory generation method for an automatic driving test scene, and the method comprises the following steps: extracting a plurality of groups of driving features in an interaction scene according to a set interaction logic, and carrying out the simulation training, so as to construct a driving simulator, driving characteristics in each group of interaction scenes are used as driving behavior data of a plurality of background vehicles, set driving styles are configured in a one-to-one correspondence manner, and a corresponding driving strategy is set for each driving style so as to construct a dynamic Bayesian game framework; the method comprises the following steps: acquiring a driving style of a background vehicle in any group of interaction scenes in real time to carry out iterative training on a multi-target utility function, calculating a profit value corresponding to the driving style of the background vehicle by utilizing the multi-target utility function, and selecting a corresponding driving strategy from a dynamic Bayesian game framework to generate a track control instruction; and performing personification optimization on the trajectory control instruction by using a random disturbance mechanism, thereby realizing multi-dimensional driving behavior dynamic generation and complex interaction logic accurate modeling.

The invention discloses a function calculation mechanism for document generation, which belongs to the technical field of complex logic content generation and comprises the following steps of: introducing a JavaScript engine: utilizing the JavaScript engine as a core processing unit for function calculation, providing strong script execution capability and wide library support by the JavaScript engine, and generating a function by using the JavaScript engine; various complex logic calculation and data processing can be efficiently executed; a Word template engine is used, a POI-TL is used as a template engine for document generation, the POI-TL is based on an Apache POI library, and a Word document can be conveniently read and written in; according to the method, efficient calculation is carried out, namely, the JavaScript engine can quickly execute complex logical operation and data processing, so that the document generation time is shortened, for example, all calculation and document generation tasks can be completed within several seconds when a contract containing complex calculation and logical judgment is generated; and automatic processing: high automation is realized in the whole process from data input to document generation, manual intervention is reduced, and the overall working efficiency is improved.

A method, computer program product, and computer system of artificial intelligence (AI) intervention to detect and mitigate an abnormality associated with an object moving during performance of a process. A trained recurrent neural network (RNN) determines, from sensor data, a probability (Pr1) that the abnormality existed at a first time, where Pr1 exceeds a threshold T1 and in response, an alternative generative adversarial network (AGAN) determines a probability (Pa1) that the abnormality existed at the first time. A score S1, which is computed as a function of Pr1 and Pa1, exceeds T1 and in response the RNN and the AGAN determines, from the senso data, a probability (Pr2) and a probability (Pa2), respectively, that the abnormality existed at a second time. A score S2, which is computed as a function of at least Pr2, Pa2, and (S1-T1), exceeds a threshold (T2) and in response, the abnormality is mitigated.

The invention relates to the technical field of artificial intelligence and privacy protection, and discloses a vertical field large model privacy forgetting method and terminal based on double Fisher matrix guidance, and the method comprises the steps: separating two text sequence data subsets from an original training corpus of a vertical field large language model, namely a reserved data set and a forgetting data set; the reserved data set contains vertical field professional data which does not contain privacy information, and the forgotten data set contains privacy sensitive data to be eliminated. Based on an information geometry theory, calculating a reserved Fisher information matrix and a forgotten Fisher information matrix of model parameters on the reserved data set and the forgotten data set respectively; based on collaborative analysis of a reserved Fisher information matrix and a forgotten Fisher information matrix, a parameter forgotten guide function is constructed, guide function values of all parameters of a model are calculated, and a standardized guide vector is constructed according to the guide function values. According to the method, refined balance of privacy forgetting of the large language model in two levels of privacy elimination and capability keeping is realized.

The application discloses a kind of based on the difference of communication guarantee system and method of computing power service level, the system includes: computing power level construction module, priority generation module, resource scheduling module and closed loop correction module;The computing power level construction module is used to the computing power service object in the algorithm network fusion scene, constructs computing power service level vector and is disassembled into multidimensional computing power service characteristic factor, after normalization processing and weight distribution, the normalized computing power service level comprehensive vector is obtained, provide basis for subsequent computing power level and communication priority mapping, the present application relates to communication guarantee technical field.A kind of based on the difference of communication guarantee system and method of computing power service level, based on the real-time computing power uncertainty value of computing power entropy function calculation system, and combining computing power entropy gradient change rate, communication guarantee priority can be adjusted in time when computing power resource fluctuation is larger, to avoid the communication interruption or delay caused by resource fluctuation.

This disclosure provides a log data processing method, apparatus, system, device, medium, and program product, relating to the field of computer technology, and particularly to cloud computing and function computing technologies. The specific implementation scheme is as follows: the method is applied to a machine, where multiple instances run. The method includes: acquiring metadata, wherein the metadata includes resource scaling parameters and a code package, the resource scaling parameters being used to measure the timing of scaling up or down, and the code package including log data processing rules; running the code package through any target instance among the multiple instances, wherein the machine has a runtime deployed, the runtime providing a framework for running the log data processing program, and the target instance running within the framework provided by the runtime; monitoring the runtime's operating metrics, and scaling up or down the machine based on the operating metrics and resource scaling parameters. This disclosure enables flexible scaling up or down by monitoring the runtime's operating metrics.

An approximation method of softmax function which converts an input value of k-dimensional vector into an output value of m-dimensional vector, comprises: an exponential function approximation computing step performing a Leaky ReLU computation on one input value of k-dimensional vector to obtain a Leaky ReLU computation value and performing a polynomial function computation of a certain order based on the Leaky ReLU computation value to obtain an exponential approximation value, the exponential function approximation computing step repeated for another input value of k-dimensional vector to obtain another exponential approximation value; an addition computing step adding the exponential approximation values to obtain a sum value; and a division computing step dividing at least one of the exponential approximation values obtained in the exponential function approximation computing step by the sum value to obtain an output value of m-dimensional vector.

The present invention discloses a method, apparatus, device and storage medium for running a function computing platform, belonging to the technical field of cloud services. In the present invention, by receiving function source code, when the function source code is of a target language type, it is compiled by a source code compilation component to obtain a bytecode file, and a corresponding function image is constructed, and the function image corresponding to the function source code is deployed and run through a cluster management background. The present invention changes the work of compiling and packaging function source code by the user locally to automatic processing by the function computing platform, thereby simplifying the process of the user publishing function source code, reducing the user's usage cost, and enabling the user to have a consistent experience when publishing functions in different programming languages on the platform.

The application discloses a log processing method and device, computer equipment and a storage medium, and belongs to the technical field of computers. The application filters the field data targeted by this log analysis by using filter words on the log to be analyzed, and then automatically performs business analysis on the filtered field data in a function calculation manner through an analysis function associated with a business analysis type, and outputs the final business analysis result, so that as long as the analysis function is configured, the analysis function can be introduced to implement business analysis by a machine, without manual participation in the log analysis process to troubleshoot business problems, improving the processing efficiency of the log analysis process, and enabling organic combination of log analysis and abnormal positioning and troubleshooting tasks of business logic.

This invention belongs to the field of transcendental function computation technology and discloses a resource-reusable transcendental function computation implementation device, including a preprocessing module, a table lookup module, a calculation module, and a post-processing module. The preprocessing module performs corresponding data processing according to the type of the computation function and sends the processed data to the other three modules. The table lookup module searches for the corresponding table according to the type of the computation function, obtains the polynomial coefficients, and sends them to the calculation module. The calculation module performs a finite number of multiplications and additions on the processed data from the preprocessing module and the coefficients from the table lookup module, and sends the results to the post-processing module. The post-processing module performs corresponding data post-processing according to the type of the computation function, obtains the results, and outputs them. This invention, by reusing hardware resources such as adders / subtractors and multipliers, can realize the calculation of sine, cosine, arctangent, exponential, and logarithmic functions.

The embodiment of the invention provides an object storage and retrieval optimization method and system based on function computing, the method is applied to the object storage and retrieval optimization system based on function computing, the system comprises an object storage access gateway, an object gateway, a function computing module and a retrieval acceleration module which are connected in sequence, and the object gateway is further connected with a storage engine; the method comprises the following steps: an object storage access gateway obtains a retrieval request input by a user and determines a request type; if the retrieval request is of the first type, the object storage access gateway sends the retrieval request to an object gateway, the object gateway executes and completes operation corresponding to the retrieval request in a storage engine, and after the operation is completed, event information is generated through an event trigger pre-configured on the object gateway; the function calculation module operates a preset function operator according to the event information to obtain a calculation result of the function operator; and the retrieval acceleration module adds the calculation result of the function operator to a pre-obtained initial table to obtain final table data.

Techniques for performing secure computing of softmax functions at high speed and with high accuracy are provided. A secure softmax function calculation system that calculates a share ([[softmax (u1)]], . . . , [[softmax (uJ)]]) from a share ([[u1]], . . . , [[uJ]]) includes a subtraction means for calculating a share ([[u1−u1]], [[u2−u1]], . . . , [[uJ−uJ]]), a first secure batch mapping calculation means for calculating, [[exp (u1−u1)]], [[exp (u2−u1)]], . . . , [[exp (uJ−uJ)]], an addition means for calculating a share([[∑ j=1J⁢exp⁡(uj-u1)]],… ,[[∑ j=1J⁢exp⁡(uj-uJ)]],and a second secure batch mapping calculation means for calculating a share ([[softmax (u1)], . . . , [[softmax (uJ)]]).

A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

The present disclosure relates to the field of function compute in serverless computing. Disclosed in the present disclosure are serverless computing-based data processing methods and an electronic device. A method comprises: acquiring a plurality of function requests from a host machine; determining a target function that any function request requests a graphics processing unit to execute, so as to obtain a plurality of target functions; and determining an execution sequence of the plurality of target functions on the basis of performance indexes of the plurality of target functions, wherein the performance indexes of each target function is used for representing the performance of the target function in the execution process, and the execution sequence is used for determining data transmitted between a main memory of the host machine and a graphics memory of the graphics processing unit.